Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine IntelligencePublished 28 March 2007
Jayadeva, R. Khemchandani, Suresh Chandra
Citations1,780
SJR quartileQ1
SJR score3.91
SNIP5.99
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TL;DR
A binary SVM classifier that determines two nonparallel planes by solving two related SVM-type problems, each of which is smaller than in a conventional SVM, which shows good generalization on several benchmark data sets.
Abstract
We propose Twin SVM, a binary SVM classifier that determines two nonparallel planes by solving two related SVM-type problems, each of which is smaller than in a conventional SVM. The Twin SVM formulation is in the spirit of proximal SVMs via generalized eigenvalues. On several benchmark data sets, Twin SVM is not only fast, but shows good generalization. Twin SVM is also useful for automatically discovering two-dimensional projections of the data.
Keywords
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